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Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down‐stream analysis can be conducted with modern statistical methods that cannot normally handle data subject to informative censoring. This work develops an empirical Bayes g$$ g $$‐modeling method for imputing and denoising biomarker measurements. We establish superior estimation properties compared to popular methods in simulations and with real data, providing the useful biomarker measurement estimations for down‐stream analysis. [ABSTRACT FROM AUTHOR]

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates the patients' molecular profiles with the patients' survival. With complex relationships between survival and high‐dimensional molecular predictors, it is challenging to conduct nonparametric modeling and irrelevant predictors removing simultaneously. In this article, we build a kernel Cox proportional hazards semi‐parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and nonparametric predictors through a LASSO penalty. An efficient high‐dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict the patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes. [ABSTRACT FROM AUTHOR]

• A self-organized mechanism based consensus framework is presented. • Non-cooperative behaviors are detected and managed at the element level of MAEMs. • A novel distance-based ordinal consensus measurement is proposed. • Simulation analysis is presented to show the effectiveness of our proposals. In consensus-based multi-attribute group decision making (MAGDM) problems, decision makers (DMs) may exhibit non-cooperative behaviors since they usually have different individual interests (sometimes conflicting) or limited knowledge, which strongly affects the efficiency of consensus and the quality of decision outcomes. Additionally, the existing MAGDM studies mainly focus on the cardinal consensus, and the ordinal consensus is ignored. Thus, this paper proposes a self-organized mechanism based framework to manage non-cooperative behaviors and ordinal consensus in MAGDM. First, a dual-membership function based on the basic idea of synergy theory is designed to detect non-cooperative behaviors at the element level of the multiple attribute evaluation matrix (MAEM), and then the weights of elements with non-cooperative behaviors are penalized automatically. In this way, the negative effects of non-cooperative behaviors can be eliminated. Next, a novel preference ranking-based ordinal consensus approach is proposed, which calculates an ordinal consensus based on the preference rankings of alternatives between individuals and the group. If the pre-defined consensus level is not reached, the feedback adjustment is used to help DMs modify their MAEMs to improve the consensus level; otherwise, the selection process is utilized to choose the optimal alternative(s). Finally, detailed simulation experiments and comparative analysis are designed to show the properties and effectiveness of the proposed framework, and an illustrative angel investment case is presented to demonstrate the calculation process and usability. [ABSTRACT FROM AUTHOR]

Recovering aged bitumen from reclaimed asphalt pavement (RAP) is crucial for the evolution of pavement recycling. This study delves deeply into the effects of rejuvenator components on the rheological properties of rejuvenated bitumen across a wide temperature range. The investigation utilized the rolling thin film oven test (RTFOT) and the pressure aging vessel (PAV) test to simulate the short-term and long-term aging of the bitumen binder, respectively. Four distinct rejuvenators were then applied to recover the bitumen at varied aging levels. Detailed chemical analyses, encompassing the four-fraction (SARA) method and Fourier transform infrared spectroscopy (FTIR), were employed to identify the chemical compositions and key functional groups of both the rejuvenators and the rejuvenated bitumen. The rheological behavior of rejuvenated bitumen was assessed through a comprehensive series of tests, including conventional, temperature sweep, frequency sweep, multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), and bending beam rheometer (BBR) tests. A Pearson correlation analysis highlighted a significant positive association between specific rejuvenator components, notably asphaltene (As), saturate (S), and aromatic (Ar), and the high-temperature performance of the rejuvenated bitumen. The findings of this research offer insights into the formulation of rejuvenators, potentially enhancing the performance of rejuvenated asphalt pavements and facilitating sustainable infrastructure advancements. • A significant correlation between high-temperature property and component was found. • The deterioration no only relied on the chemical components at low temperature. • MSCR, G-R parameter, and m-valve are more representative to evaluate properties. • The findings of this research offer insights into the formulation of rejuvenators. [ABSTRACT FROM AUTHOR]

Intimal hyperplasia (IH) is a common pathological feature of vascular proliferative diseases, such as atherosclerosis and restenosis after angioplasty. Urotensin II (UII) and its receptor (UTR) are widely expressed in cardiovascular tissues. However, it remains unclear whether the UII/UTR system is involved in IH. Right unilateral common carotid artery ligation was performed and maintained for 21 days to induce IH in UTR knockout (UTR−/−) and wild-type (WT) mice. Histological analysis revealed that compared with WT mice, UTR-deficient mice exhibited a decreased neointimal area, angiostenosis and intima–media ratio. Immunostaining revealed fewer smooth muscle cells (SMCs), endothelial cells and macrophages in the lesions of UTR−/− mice than in those of WT mice. Protein interaction analysis suggested that the UTR may affect cell proliferation by regulating YAP and its downstream target genes. In vitro experiments revealed that UII can promote the proliferation and migration of SMCs, and western blotting also revealed that UII increased the protein expression of RhoA, CTGF, Cyclin D1 and PCNA and downregulated p-YAP protein expression, while these effects could be partly reversed by urantide. To evaluate the translational value of UTRs in IH management, WT mice were also treated with two doses of urantide, a UTR antagonist, to confirm the benefit of UTR blockade in IH progression. A high dose of urantide (600 μg/kg/day), rather than a low dose (60 μg/kg/day), successfully improved ligation-induced IH compared with that in mice receiving vehicle. The results of the present study suggested that the UII/UTR system may regulate IH partly through the RhoA-YAP signaling pathway. [Display omitted] • We found that urotensin II receptor (UTR) deficiency ameliorates ligation-induced carotid intimal hyperplasia (IH) in mice. • Cultured SMCs experiment suggested that the UII/UTR system may regulate IH partly through the RhoA-YAP signaling pathway. • Urantide, a UTR antagonist, was found that can improve IH in mice. [ABSTRACT FROM AUTHOR]